Empirical potential structure refinement EPSR

In all these examples, the importance of good simulation and modeling cannot be stressed enough. A variety of methods have been used in this field to simulate the data in the cases studies described above. Blander et al. [4], for example, used a semi-empirical molecular orbital method, MNDO, to calculate the geometries of the free haloaluminate ions and used these as a basis for the modeling of the data by the RPSU model [12]. Badyal et al. [6] used reverse Monte Carlo simulations, whereas Bowron et al. [11] simulated the neutron data from [MMIM]C1 with the Empirical Potential Structure Refinement (EPSR) model [13]. 

Using Monte Carlo, the model is then relaxed using the new potential, resulting in a new calculated g r). The process is then iterated until it reaches convergence. This approach, known as Empirical Potential Structure Refinement (EPSR) has proved very powerful in the study of complex liquids, and of the solvation states of molecules in solution. It is particularly successful when it has as target functions a number (though not necessarily the complete set) of DPDFs from the system in question. 

In order to extract microscopic quantities such as spatial probabilities and RDFs from collected differential cross scattering data, analysis is typically performed following reverse Monte Carlo (RMC) or empirical potential structure refinement (EPSR) procedures [7]. The latter procedure can be viewed as a Monte Carlo simulation of system utilising a model potential similar to a classical molecular mechanics force field. This model potential is modified in order to bring the total structure factor calculated from the model system as close as possible to the ejq)erimental data. From configurations generated with this refined potential, standard quantities (such as RDFs) may be calculated. 

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